Data integration refers to the combination of data from one or more sources into a homogenous environment at a target destination. For example, a financial institution may combine data about financial transactions from multiple sources into a data warehouse. Extract, transform, and load (ETL) refers to a process that extracts data from one or more sources, transforms it to fit operational needs of an organization, and loads it into an end target, such as a database or data warehouse. Data integration systems, such as ETL systems, may process multiple terabytes of data using multiple servers with multiple processing units. Data integration systems may also comprise or associate with scheduler tools, which may manage some or all of the functions of a data integration system. For example, scheduler tools may provide workload automation. Data integration systems may also use and/or comprise files associated with the data integration system that assist with the function of the data integration system and/or comprise some or all of the data being integrated. These data integration system files may be transferred, or migrated, from one set of servers and processing units to another. For example, a financial institution may have upgraded its servers and wish to transfer data integration system files from its existing servers to its upgraded servers. In certain situations, existing servers and upgraded servers may execute or use different operating systems.